Creating a Vector of Colors with Specified Hue, Chroma, and Luminance in R Using `hcl()`

Table of Contents#

1. Understanding the HCL Color Model
2. hcl() Function Syntax and Parameters
3. Generating Color Vectors
4. Practical Use Cases
5. Best Practices and Common Pitfalls
6. Conclusion
7. References

Understanding the HCL Color Model#

The HCL color space decouples three perceptual attributes:

1. Hue: The "color" itself (0-360° on the color wheel)
   • 0° = Red, 120° = Green, 240° = Blue, 360° = Red
2. Chroma: Color purity or intensity (0-100+)
   • 0 = grayscale, higher values = more vivid
3. Luminance: Perceived brightness (0-100)
   • 0 = black, 100 = white, 50 = most vivid

Unlike RGB (which mixes light) or HSL (non-perceptually uniform), HCL's mathematical formulation ensures that:

• Luminance variations are perceptually linear
• Chroma boundaries stay within displayable gamut
• Hue differences correspond to consistent perceptual shifts

Visual representation of HCL dimensions (Source: Zeileis et al., 2009)

hcl() Function Syntax and Parameters#

The base R hcl() function has the following syntax:

hcl(h = 0, c = 35, l = 65, alpha = 1, fixup = TRUE)

Parameters Explained:#

Key Characteristics:#

• All parameters are vectorized (critical for palette generation)
• Chroma and luminance values wrap/recycle to match longest vector
• For single-color output, pass scalar values to all parameters
• When fixup = TRUE (default), colors are automatically adjusted to nearest displayable RGB values

Generating Color Vectors#

Basic Single-Color Example#

# Create a vivid red
red <- hcl(h = 0, c = 100, l = 50)
red
# [1] "#E32926"

Creating Gradients#

Generate a 10-color vector from blue to yellow with constant chroma and luminance:

gradient <- hcl(
  h = seq(240, 60, length.out = 10), 
  c = 70, 
  l = 65
)

Smooth perceptual gradient with constant chroma/luminance

Creating Diverging Palettes#

diverging <- hcl(
  h = c(260, 0, 60),  # Purple -> Gray (close to white) -> Yellow
  c = c(80, 0, 80),   # Chroma: vivid -> gray -> vivid
  l = c(25, 95, 85),  # Dark -> Light -> Medium
  alpha = 0.9         # Slight transparency
)

Qualitative Palettes#

Create 8 distinct colors with consistent chroma and luminance:

qualitative <- hcl(
  h = seq(0, 315, length.out = 8), 
  c = 60, 
  l = 70
)

Parameter Vectorization#

Match vectors manually for complex effects:

custom_vector <- hcl(
  h = c(120, 240, 30, 300),
  c = c(40, 80, 60, 90),
  l = c(70, 60, 90, 40),
  alpha = c(1, 0.7, 1, 0.5)
)

Practical Use Cases#

1. Heatmap Creation#

# Generate 256-color heatmap palette
heat_colors <- hcl(
  h = 0,               # Red hue
  c = 100,             # Maximum chroma
  l = seq(100, 0, length.out = 256) # Light to dark
)
filled.contour(volcano, col = heat_colors)

2. Accessible Categorical Palettes#

Colorblind-friendly palette with distinct hues and balanced luminance:

accessible <- hcl(
  h = c(20, 120, 260), 
  c = 70, 
  l = c(60, 65, 70)
)
pie(rep(1,3), col = accessible, labels = c("Group A", "Group B", "Group C"))

3. Grayscale Conversion#

image_colors <- c("#FF5733", "#33FF57", "#3357FF")
grayscale <- hcl(
  h = 0,                            # Hue value (arbitrary when chroma=0)
  c = 0,                            # Zero chroma
  l = 70                            # Fixed luminance
)

4. Coordinating Plot Elements#

plot(1:10, col = hcl(h=180, c=50, l=70), pch=16, cex=2)
lines(1:10, col = hcl(h=180, c=80, l=50), lwd=2)
title(main = "Teal Color Scheme", col.main = hcl(h=180, c=30, l=30))

Best Practices and Common Pitfalls#

Do:#

1. Prioritize luminance contrast - Ensure at least 30-point difference between foreground/background
2. Keep chroma consistent within palettes for visual harmony
3. Test palettes with scales::show_col(your_palette)
4. Vectorize intentionally - Use rep() for repeated values
5. Adjust for output medium - Print requires higher chroma than screens

Don't:#

1. Assume gamut coverage - High chroma values may not render as expected (fixup=TRUE helps)
2. Ignore lighting conditions - Ambient light affects perceived luminance
3. Overload with colors - Stick to 6-8 categorical colors maximum
4. Mix HCL/RGB carelessly - Convert between spaces explicitly with convertColor()

Common Fixes:#

# Check if colors were modified by fixup
original <- hcl(280, 150, 50, fixup = FALSE) # May be NA
adjusted <- hcl(280, 150, 50) # Adjusted to #A63BEB
 
# Create valid colors by constraining parameters
safe_palette <- hcl(
  h = seq(0, 300, length.out = 6),
  c = pmin(90, 150),  # Cap chroma at 90
  l = 60
)

Conclusion#

The hcl() function provides a powerful, perception-based approach to color generation in R. By separating hue, chroma, and luminance, you gain intuitive control over color properties that matter most in visualization. Key advantages include:

• Creating perceptually balanced palettes with minimal effort
• Ensuring accessibility through luminance control
• Generating seamless gradients and specialized palettes
• Maintaining consistency across visualization elements

While mastering HCL requires practice, the payoff is visually compelling and statistically accurate data representation. Remember that:

• HCL complements rather than replaces other color spaces
• Always validate palettes with actual data plots
• Context (media, audience, lighting) should drive parameter choices

Experiment with the included examples and consult the references below to deepen your understanding of color theory in data visualization.

References#

1. Zeileis, A., Hornik, K., & Murrell, P. (2009). Escaping RGBland: Selecting colors for statistical graphics. Computational Statistics & Data Analysis, 53(9), 3259-3270.
2. Ihaka, R. (2003). Colour for Presentation Graphics. Proceedings of the 3rd International Workshop on Distributed Statistical Computing.
3. R Core Team (2023). R: A Language and Environment for Statistical Computing. https://www.R-project.org/
4. ?hcl - Official R Documentation
5. Stauffer, R. et al. (2015). ggthemes: Extra Themes, Scales and Geoms for ggplot2. R package version 3.4.0.
6. Lumley, T. (2013). colorspace: Color Space Manipulation. R package version 1.4-1.